Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations6497
Missing cells0
Missing cells (%)0.0%
Duplicate rows993
Duplicate rows (%)15.3%
Total size in memory609.2 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

Dataset has 993 (15.3%) duplicate rowsDuplicates
alcohol is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with densityHigh correlation
density is highly overall correlated with alcohol and 2 other fieldsHigh correlation
free_sulfur_dioxide is highly overall correlated with total_sulfur_dioxideHigh correlation
residual_sugar is highly overall correlated with densityHigh correlation
total_sulfur_dioxide is highly overall correlated with free_sulfur_dioxideHigh correlation
citric_acid has 151 (2.3%) zerosZeros

Reproduction

Analysis started2024-11-23 18:02:54.197843
Analysis finished2024-11-23 18:04:14.643302
Duration1 minute and 20.45 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

fixed_acidity
Real number (ℝ)

Distinct106
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2153071
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:15.147301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2964338
Coefficient of variation (CV)0.17967825
Kurtosis5.0611607
Mean7.2153071
Median Absolute Deviation (MAD)0.6
Skewness1.7232896
Sum46877.85
Variance1.6807405
MonotonicityNot monotonic
2024-11-23T23:34:15.776940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 354
 
5.4%
6.6 327
 
5.0%
6.4 305
 
4.7%
7 282
 
4.3%
6.9 279
 
4.3%
7.2 273
 
4.2%
6.7 264
 
4.1%
7.1 257
 
4.0%
6.5 242
 
3.7%
7.4 238
 
3.7%
Other values (96) 3676
56.6%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
< 0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 9
 
0.1%
4.9 8
 
0.1%
5 30
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 2
< 0.1%
15 2
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
< 0.1%
13.5 1
< 0.1%

volatile_acidity
Real number (ℝ)

Distinct187
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.339666
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:16.337947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.16463647
Coefficient of variation (CV)0.48470107
Kurtosis2.8253724
Mean0.339666
Median Absolute Deviation (MAD)0.08
Skewness1.4950965
Sum2206.81
Variance0.027105169
MonotonicityNot monotonic
2024-11-23T23:34:17.069370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 286
 
4.4%
0.24 266
 
4.1%
0.26 256
 
3.9%
0.25 238
 
3.7%
0.22 235
 
3.6%
0.27 232
 
3.6%
0.23 221
 
3.4%
0.2 217
 
3.3%
0.3 214
 
3.3%
0.32 205
 
3.2%
Other values (177) 4127
63.5%
ValueCountFrequency (%)
0.08 4
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 6
 
0.1%
0.11 13
 
0.2%
0.115 3
 
< 0.1%
0.12 37
0.6%
0.125 3
 
< 0.1%
0.13 44
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric_acid
Real number (ℝ)

ZEROS 

Distinct89
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31863322
Minimum0
Maximum1.66
Zeros151
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:17.968343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.14531786
Coefficient of variation (CV)0.45606628
Kurtosis2.3972392
Mean0.31863322
Median Absolute Deviation (MAD)0.07
Skewness0.47173067
Sum2070.16
Variance0.021117282
MonotonicityNot monotonic
2024-11-23T23:34:18.782914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 337
 
5.2%
0.28 301
 
4.6%
0.32 289
 
4.4%
0.49 283
 
4.4%
0.26 257
 
4.0%
0.34 249
 
3.8%
0.29 244
 
3.8%
0.27 236
 
3.6%
0.24 232
 
3.6%
0.31 230
 
3.5%
Other values (79) 3839
59.1%
ValueCountFrequency (%)
0 151
2.3%
0.01 40
 
0.6%
0.02 56
 
0.9%
0.03 32
 
0.5%
0.04 41
 
0.6%
0.05 25
 
0.4%
0.06 30
 
0.5%
0.07 34
 
0.5%
0.08 37
 
0.6%
0.09 42
 
0.6%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 2
 
< 0.1%

residual_sugar
Real number (ℝ)

HIGH CORRELATION 

Distinct316
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4432353
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:19.611867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.7578037
Coefficient of variation (CV)0.87407644
Kurtosis4.3592719
Mean5.4432353
Median Absolute Deviation (MAD)1.7
Skewness1.4354043
Sum35364.7
Variance22.636696
MonotonicityNot monotonic
2024-11-23T23:34:20.470868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 235
 
3.6%
1.8 228
 
3.5%
1.6 223
 
3.4%
1.4 219
 
3.4%
1.2 195
 
3.0%
2.2 187
 
2.9%
2.1 179
 
2.8%
1.9 176
 
2.7%
1.7 175
 
2.7%
1.5 172
 
2.6%
Other values (306) 4508
69.4%
ValueCountFrequency (%)
0.6 2
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.4%
0.9 41
 
0.6%
0.95 4
 
0.1%
1 93
1.4%
1.05 1
 
< 0.1%
1.1 146
2.2%
1.15 3
 
< 0.1%
1.2 195
3.0%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
< 0.1%
26.05 2
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 2
< 0.1%
20.8 2
< 0.1%
20.7 2
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct214
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056033862
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:21.145413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.035033601
Coefficient of variation (CV)0.62522197
Kurtosis50.898051
Mean0.056033862
Median Absolute Deviation (MAD)0.011
Skewness5.3998277
Sum364.052
Variance0.0012273532
MonotonicityNot monotonic
2024-11-23T23:34:21.874391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 206
 
3.2%
0.036 200
 
3.1%
0.042 187
 
2.9%
0.046 185
 
2.8%
0.05 182
 
2.8%
0.04 182
 
2.8%
0.048 182
 
2.8%
0.047 175
 
2.7%
0.045 174
 
2.7%
0.038 169
 
2.6%
Other values (204) 4655
71.6%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 4
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 10
0.2%
0.019 9
0.1%
0.02 16
0.2%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
< 0.1%
0.414 2
< 0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free_sulfur_dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct135
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.525319
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:22.647914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.7494
Coefficient of variation (CV)0.58146483
Kurtosis7.9062381
Mean30.525319
Median Absolute Deviation (MAD)12
Skewness1.2200661
Sum198323
Variance315.04119
MonotonicityNot monotonic
2024-11-23T23:34:24.256092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 183
 
2.8%
6 170
 
2.6%
26 161
 
2.5%
15 157
 
2.4%
24 152
 
2.3%
31 152
 
2.3%
17 149
 
2.3%
34 146
 
2.2%
35 144
 
2.2%
23 142
 
2.2%
Other values (125) 4941
76.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 59
 
0.9%
4 52
 
0.8%
5 129
2.0%
5.5 1
 
< 0.1%
6 170
2.6%
7 96
1.5%
8 91
1.4%
9 91
1.4%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total_sulfur_dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.74457
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:24.941188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.521855
Coefficient of variation (CV)0.48833265
Kurtosis-0.37166365
Mean115.74457
Median Absolute Deviation (MAD)39
Skewness-0.0011774782
Sum751992.5
Variance3194.72
MonotonicityNot monotonic
2024-11-23T23:34:25.765187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 72
 
1.1%
113 65
 
1.0%
117 57
 
0.9%
122 57
 
0.9%
128 56
 
0.9%
98 56
 
0.9%
124 56
 
0.9%
114 56
 
0.9%
118 55
 
0.8%
150 54
 
0.8%
Other values (266) 5913
91.0%
ValueCountFrequency (%)
6 3
 
< 0.1%
7 4
 
0.1%
8 14
 
0.2%
9 15
0.2%
10 28
0.4%
11 26
0.4%
12 29
0.4%
13 28
0.4%
14 33
0.5%
15 35
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct998
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99469663
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:26.704716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999392
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.002998673
Coefficient of variation (CV)0.0030146609
Kurtosis6.606067
Mean0.99469663
Median Absolute Deviation (MAD)0.00231
Skewness0.50360173
Sum6462.544
Variance8.9920398 × 10-6
MonotonicityNot monotonic
2024-11-23T23:34:27.872081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9976 69
 
1.1%
0.9972 69
 
1.1%
0.998 64
 
1.0%
0.992 64
 
1.0%
0.9928 63
 
1.0%
0.9986 61
 
0.9%
0.9962 59
 
0.9%
0.9966 59
 
0.9%
0.9956 55
 
0.8%
0.9968 55
 
0.8%
Other values (988) 5879
90.5%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
< 0.1%
ValueCountFrequency (%)
1.03898 1
 
< 0.1%
1.0103 2
< 0.1%
1.00369 2
< 0.1%
1.0032 1
 
< 0.1%
1.00315 3
< 0.1%
1.00295 2
< 0.1%
1.00289 1
 
< 0.1%
1.0026 2
< 0.1%
1.00242 2
< 0.1%
1.00241 1
 
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2185008
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:28.748075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1607872
Coefficient of variation (CV)0.049957173
Kurtosis0.36765727
Mean3.2185008
Median Absolute Deviation (MAD)0.11
Skewness0.3868388
Sum20910.6
Variance0.025852524
MonotonicityNot monotonic
2024-11-23T23:34:29.560085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 200
 
3.1%
3.14 193
 
3.0%
3.22 185
 
2.8%
3.2 176
 
2.7%
3.15 170
 
2.6%
3.19 170
 
2.6%
3.18 168
 
2.6%
3.24 161
 
2.5%
3.1 154
 
2.4%
3.12 154
 
2.4%
Other values (98) 4766
73.4%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
 
< 0.1%
2.8 3
 
< 0.1%
2.82 1
 
< 0.1%
2.83 4
 
0.1%
2.84 1
 
< 0.1%
2.85 9
0.1%
2.86 10
0.2%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 2
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53126828
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:30.258167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14880587
Coefficient of variation (CV)0.28009554
Kurtosis8.6536988
Mean0.53126828
Median Absolute Deviation (MAD)0.08
Skewness1.79727
Sum3451.65
Variance0.022143188
MonotonicityNot monotonic
2024-11-23T23:34:31.110829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 276
 
4.2%
0.46 243
 
3.7%
0.54 235
 
3.6%
0.44 232
 
3.6%
0.38 214
 
3.3%
0.48 208
 
3.2%
0.52 203
 
3.1%
0.49 197
 
3.0%
0.47 191
 
2.9%
0.45 190
 
2.9%
Other values (101) 4308
66.3%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 4
 
0.1%
0.27 13
 
0.2%
0.28 13
 
0.2%
0.29 16
 
0.2%
0.3 31
0.5%
0.31 35
0.5%
0.32 54
0.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
< 0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.491801
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:33.362424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1927117
Coefficient of variation (CV)0.11368037
Kurtosis-0.53168738
Mean10.491801
Median Absolute Deviation (MAD)0.9
Skewness0.56571773
Sum68165.23
Variance1.4225613
MonotonicityNot monotonic
2024-11-23T23:34:34.864002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 367
 
5.6%
9.4 332
 
5.1%
9.2 271
 
4.2%
10 229
 
3.5%
10.5 227
 
3.5%
11 217
 
3.3%
9 215
 
3.3%
9.8 214
 
3.3%
10.4 194
 
3.0%
9.3 193
 
3.0%
Other values (101) 4038
62.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 5
 
0.1%
8.5 10
 
0.2%
8.6 23
 
0.4%
8.7 80
 
1.2%
8.8 109
1.7%
8.9 95
1.5%
9 215
3.3%
9.05 1
 
< 0.1%
9.1 167
2.6%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 12
0.2%
13.9 3
 
< 0.1%
13.8 2
 
< 0.1%
13.7 7
0.1%
13.6 13
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8183777
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-23T23:34:36.865715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87325527
Coefficient of variation (CV)0.1500857
Kurtosis0.23232227
Mean5.8183777
Median Absolute Deviation (MAD)1
Skewness0.18962269
Sum37802
Variance0.76257477
MonotonicityNot monotonic
2024-11-23T23:34:37.464816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2836
43.7%
5 2138
32.9%
7 1079
 
16.6%
4 216
 
3.3%
8 193
 
3.0%
3 30
 
0.5%
9 5
 
0.1%
ValueCountFrequency (%)
3 30
 
0.5%
4 216
 
3.3%
5 2138
32.9%
6 2836
43.7%
7 1079
 
16.6%
8 193
 
3.0%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 193
 
3.0%
7 1079
 
16.6%
6 2836
43.7%
5 2138
32.9%
4 216
 
3.3%
3 30
 
0.5%

Interactions

2024-11-23T23:34:07.644336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:32:55.216140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:02.836193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:10.651734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:18.819803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:25.272781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:32.870305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:38.918727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:45.398537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:52.364197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:58.231679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:03.128483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:08.191340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:32:55.886769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:04.004753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:11.224966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:19.157832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:25.679780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:33.261616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:39.552790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:45.924810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:52.807197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:58.776680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:03.485356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:08.830660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:32:56.557774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:05.369969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:11.886966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:19.596434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:26.096782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:33.630999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:40.565091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:46.504337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:53.363195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:59.404674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:03.895361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:09.362657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:32:57.612291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:06.549278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:12.368965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:20.056434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:26.575788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:33.968985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:41.151603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:47.119690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:53.864496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:59.770237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:04.248358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:09.970929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:32:58.053291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:07.246755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:13.338172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:21.075323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:27.021097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:34.382985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:41.754604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:48.492810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:54.348542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:00.146725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:04.595842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:10.417933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:32:58.613290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:07.607802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:14.452167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:22.183213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:27.459096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:34.863989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:42.143157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:49.035809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:54.874918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:00.572793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:05.015844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:10.890931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:32:59.306290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:07.984616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:15.054176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:22.807211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:28.066096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:35.279984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:42.525203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:49.666147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:55.364153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:00.982323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:05.372846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:11.264573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:00.161055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:08.279625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:15.415919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:23.229210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:29.680108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:35.828954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:42.825203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:50.093149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:55.943153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:01.296334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:05.687328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:11.610575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:00.565785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:08.663212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:15.966488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:23.661212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:30.675539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:36.596960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:43.213203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:50.425719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:56.395931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:01.618155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:06.010688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:11.962599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:01.126795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:09.152211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:16.908299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:24.060210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:31.166048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:37.192373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:43.637199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:50.864856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:56.876918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:01.970257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:06.361690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:12.429640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:01.632785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:09.616207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:17.778302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:24.482213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:31.745121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:37.868376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:44.314203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:51.444914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:57.359106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:02.387794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:06.708232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:12.941792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:02.118784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:10.097728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:18.458843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:24.866783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:32.235309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:38.477999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:44.975206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:51.915922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:33:57.793146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:02.780308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-23T23:34:07.122819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-23T23:34:38.062818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric_aciddensityfixed_acidityfree_sulfur_dioxidepHqualityresidual_sugarsulphatestotal_sulfur_dioxidevolatile_acidity
alcohol1.000-0.4010.020-0.699-0.111-0.1860.1400.447-0.3290.005-0.309-0.024
chlorides-0.4011.000-0.0740.5910.356-0.2600.164-0.295-0.0360.370-0.2680.416
citric_acid0.020-0.0741.0000.0660.2710.122-0.2860.1060.0750.0370.159-0.295
density-0.6990.5910.0661.0000.4340.0060.012-0.3230.5270.2750.0620.261
fixed_acidity-0.1110.3560.2710.4341.000-0.260-0.250-0.098-0.0320.220-0.2330.200
free_sulfur_dioxide-0.186-0.2600.1220.006-0.2601.000-0.1650.0870.388-0.2210.741-0.366
pH0.1400.164-0.2860.012-0.250-0.1651.0000.033-0.2290.254-0.2430.195
quality0.447-0.2950.106-0.323-0.0980.0870.0331.000-0.0170.030-0.055-0.258
residual_sugar-0.329-0.0360.0750.527-0.0320.388-0.229-0.0171.000-0.1380.455-0.064
sulphates0.0050.3700.0370.2750.220-0.2210.2540.030-0.1381.000-0.2570.255
total_sulfur_dioxide-0.309-0.2680.1590.062-0.2330.741-0.243-0.0550.455-0.2571.000-0.344
volatile_acidity-0.0240.416-0.2950.2610.200-0.3660.195-0.258-0.0640.255-0.3441.000

Missing values

2024-11-23T23:34:13.408793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-23T23:34:14.227773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholquality
07.40.700.001.90.07611.034.00.99783.510.569.45.0
17.80.880.002.60.09825.067.00.99683.200.689.85.0
27.80.760.042.30.09215.054.00.99703.260.659.85.0
311.20.280.561.90.07517.060.00.99803.160.589.86.0
47.40.700.001.90.07611.034.00.99783.510.569.45.0
57.40.660.001.80.07513.040.00.99783.510.569.45.0
67.90.600.061.60.06915.059.00.99643.300.469.45.0
77.30.650.001.20.06515.021.00.99463.390.4710.07.0
87.80.580.022.00.0739.018.00.99683.360.579.57.0
97.50.500.366.10.07117.0102.00.99783.350.8010.55.0
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholquality
64876.80.2200.361.200.05238.0127.00.993303.040.549.25.0
64884.90.2350.2711.750.03034.0118.00.995403.070.509.46.0
64896.10.3400.292.200.03625.0100.00.989383.060.4411.86.0
64905.70.2100.320.900.03838.0121.00.990743.240.4610.66.0
64916.50.2300.381.300.03229.0112.00.992983.290.549.75.0
64926.20.2100.291.600.03924.092.00.991143.270.5011.26.0
64936.60.3200.368.000.04757.0168.00.994903.150.469.65.0
64946.50.2400.191.200.04130.0111.00.992542.990.469.46.0
64955.50.2900.301.100.02220.0110.00.988693.340.3812.87.0
64966.00.2100.380.800.02022.098.00.989413.260.3211.86.0

Duplicate rows

Most frequently occurring

fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholquality# duplicates
4617.00.150.2814.70.05129.0149.00.997922.960.399.07.08
6237.30.190.2713.90.05745.0155.00.998072.940.418.88.08
3616.80.180.3012.80.06219.0171.00.998083.000.529.07.07
6627.40.160.3013.70.05633.0168.00.998252.900.448.77.07
6617.40.160.2715.50.05025.0135.00.998402.900.438.77.06
6657.40.190.3012.80.05348.5229.00.998603.140.499.17.06
6667.40.190.3114.50.04539.0193.00.998603.100.509.26.06
7297.60.200.3014.20.05653.0212.50.999003.140.468.98.06
325.70.220.2016.00.04441.0113.00.998623.220.468.96.05
1196.20.230.3617.20.03937.0130.00.999463.230.438.86.05